In [12]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 5, 101)
s = np.sin(t**2) * np.exp(-(t-t[50])**2)
lm = ['.', '-', '--', ':', '-.']
plt.figure(figsize=(12, 5))
for i in range(len(lm)):
plt.plot(t, s+i, lm[i], linewidth=5, label='line style: ' + lm[i])
plt.legend(fontsize=25)
plt.xlim(-0.25, 8.5)
plt.show()
In [56]:
import seaborn as sns
import matplotlib.pyplot as plt
x = np.arange(0., np.e, 0.01)
y1 = np.exp(-x)
y2 = np.log(x)
fig = plt.figure(figsize=(8, 6))
ax1 = fig.add_subplot(111)
ax1.plot(x, y1)
ax1.set_ylabel('Y values for exp(-x)', fontsize=20)
ax1.set_title("Double Y axis", fontsize=20)
ax2 = ax1.twinx() # this is the important function
ax2.plot(x, y2, 'r')
ax2.set_xlim([0, np.e])
ax2.set_ylabel('Y values for ln(x)', fontsize=20)
ax2.set_xlabel('Same X for both exp(-x) and ln(x)', fontsize=20)
plt.show()
In [85]:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(100, 100000, 1001)
y = np.log(x)
plt.figure(figsize=(10,7))
plt.subplot(311)
plt.semilogx(x, y, 'r', label='log(x) linear y')
plt.legend(fontsize=20)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.subplot(312)
plt.semilogy(x, y, 'g', label='log(y) linear x')
plt.legend(fontsize=20)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.subplot(313)
plt.loglog(x, y, 'b', label='log(x) log(y)')
plt.suptitle('Log axis', fontsize=25)
plt.legend(fontsize=20)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.show()
In [102]:
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(10,8))
fig, ax = plt.subplots()
ax.plot(np.linspace(0,2*np.pi, 101), np.sin(np.linspace(0, 2*np.pi, 101)), 'r', linewidth=2)
for line in ax.get_xticklines() + ax.get_yticklines():
line.set_markeredgewidth(10)
line.set_markersize(5)
line.set_markeredgecolor('b')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.suptitle('Special ticks', fontsize=25)
plt.show()
In [83]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
s = abs(np.sin(t))
plt.figure(figsize=(20, 5))
plt.subplot(121)
ax = plt.semilogy(t, s)
# Major grid lines.
plt.grid(b=True, which='major', color='r', alpha=0.5, linewidth=1, visible=True, ls='--')
# Minor grid lines.
plt.grid(b=True, which='minor', color='g', alpha=0.5, linewidth=1, visible=True, ls=':')
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.subplot(122)
plt.plot(t, s)
ax = plt.gca()
# Grid lines on Y axis.
ax.yaxis.grid(ls=':', color='r')
ax.set_yticks(np.linspace(-1, 1, 11))
# Grid lines on X axis.
ax.xaxis.grid(ls='--', color='g')
ax.set_xticks(np.linspace(0, 10, 11))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.suptitle('Logarithm ticks and grid lines', fontsize=22)
plt.show()
In [67]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 101)
n = np.random.random(101)
plt.figure(figsize=(15, 6))
plt.subplot(121)
plt.stem(t, n, markerfmt='*', linefmt=':')
plt.subplot(122)
t = np.linspace(0, 10, 101)
n = np.random.random(101)
plt.stem(t, n, markerfmt='^', linefmt='-')
plt.suptitle('stem', fontsize=28)
plt.show()
In [41]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 5, 51)
s = np.sin(t**2) * np.exp(-(t-t[25])**2)
markers = ['.', 'o', 's', 'P', 'X', '*', 'p', 'D', '<', '>', '^',
'v', '1', '2', '3', '4', '+', 'x', '|', '_']
colors = ['r', 'g', 'b', 'c', 'k', 'm', 'y']
n = len(markers)
l = n // 3
if l*3 < n:
l += 1
plt.figure(figsize=(15, 25))
for i in range(n):
index1 = int(np.random.random()*7)
index2 = int(np.random.random()*7)
plt.subplot(l, 3, i+1)
plt.plot(t, s, color='#555555', linewidth=1)
plt.scatter(t, s, marker=markers[i], s=45, facecolor=colors[index1], edgecolor=colors[index2], linewidth=1, label=markers[i])
plt.legend(fontsize=15)
plt.subplot(l, 3, i+2)
plt.scatter(t, s, marker=7, s=25, facecolor=colors[index1], edgecolor=colors[index2], linewidth=1)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlabel('Time [s]', fontsize=20)
#plt.ylabel('Amplitude', fontsize=20)
plt.legend(fontsize=15)
plt.suptitle('Marker styles', fontsize=25)
plt.show()
In [14]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(-3, 3, 601)
X, Y = np.meshgrid(t, t)
Z = np.sin(X**2+Y**2) * np.exp(-X**2-Y**2)
cmaps_str='Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, \
CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, \
OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, \
Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, \
PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, \
RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, \
Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, \
afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, \
cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, \
flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, \
gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, \
gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, \
jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, \
plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, \
tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r'
cmaps = cmaps_str.split(',')
n = len(cmaps)
m = n // 10
if m*10 < n:
m += 1
plt.figure(figsize=(25, 50))
for i in range(n):
plt.subplot(m, 10, i+1)
cmap = cmaps[i].strip()
plt.pcolormesh(X, Y, Z, cmap=cmap)
plt.title(cmap)
plt.show()
In [60]:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
t = np.linspace(-3, 3, 601)
X, Y = np.meshgrid(t, t)
Z = np.sin(X**2+Y**2) * np.exp(-X**2-Y**2)
cdict = {'red': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 0, 0),
(0.66, 1, 1),
(0.89, 1, 1),
(1, 0.5, 0.5)),
'green': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 0, 0),
(0.375, 1, 1),
(0.64, 1, 1),
(0.91, 0, 0),
(1, 0, 0)),
'blue': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 1, 1),
(0.34, 1, 1),
(0.65, 0, 0),
(1, 0, 0))}
my_cmap = matplotlib.colors.LinearSegmentedColormap('map',cdict,256)
plt.figure(figsize=(15, 4.8))
plt.subplot(131)
plt.pcolormesh(X, Y, Z, cmap=my_cmap)
plt.title('Create your colormap', fontsize=15)
plt.subplot(132)
plt.pcolormesh(X, Y, Z, cmap='jet')
plt.subplot(133)
plt.pcolormesh(X, Y, Z, cmap='RdGy_r')
plt.show()
In [73]:
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
x = np.linspace(-10, 10, 2001)
y = x
[X, Y] = np.meshgrid( x, y )
Z = np.exp( -(X**2+Y**2) )
# 自定义colormap.
cm = mpl.colors.LinearSegmentedColormap.from_list('cmap', ['#333333', '#660588', '#98F5FF', '#00FF00', '#FFFF00','#FF0000', '#8B0000'], 256)
plt.figure(figsize=(5, 5))
plt.pcolormesh( X, Y, Z, cmap=cm )
plt.title('Make youe own color map', fontsize=23)
plt.show()
In [90]:
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal._savitzky_golay import savgol_filter as sgolay
from wavelets import WaveletAnalysis as WA
from obspy import read
st = read()
tr = st[0]
dt = tr.stats.delta
print('dt = ', dt)
fs = 1 / dt
tr.detrend()
tr.data = tr.data - np.mean(tr.data)
tr.taper(0.025)
f1 = 1
f2 = 10
tr.filter('bandpass', freqmin=f1, freqmax=f2, corners=4, zerophase=True)
t = np.arange(tr.stats.npts) * dt
s = tr.data
s = s /max(abs(s))
ss = sgolay(s, n//60*2+1, 3)
#ss = s
wa = WA(s, dt=dt)
p = (wa.wavelet_power)
tt = wa.time
ff = 1 / wa.scales
re_s = wa.reconstruction()
plt.figure(figsize=(18, 15))
plt.subplot(311)
plt.plot(t, ss, linewidth=1, label='smoothed signal')
plt.plot(t, s, linewidth=4, label='signal')
plt.plot(t, re_s, linewidth=1, label='reconstructed signal')
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize=20)
plt.xlim(t[0], t[-1])
plt.subplot(312)
plt.pcolormesh(tt, ff, p, cmap='jet')
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Frequency [Hz]', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.ylim(f1, f2)
ax = plt.subplot(313)
tr.spectrogram(log=False, axes=ax, cmap='jet')
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Frequency [Hz]', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.ylim(f1, f2)
plt.ylim(f1, f2)
plt.show()
In [17]:
import numpy as np
import matplotlib.pyplot as plt
from wavelets import WaveletAnalysis as WA
n = 2001
dt = 0.02
t = np.linspace(0, 10, n)
n = np.sin(-t**2) + np.random.random(n)/10
wa = WA(n, dt=dt)
scale = 1./wa.scales
power = wa.wavelet_power
T, F = np.meshgrid(t, scale)
plt.figure(figsize=(15, 6))
plt.subplot(121)
plt.plot(t, n, 'r', linewidth=1.5)
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.subplot(122)
plt.pcolormesh(T, F, power, cmap='jet')
plt.colorbar(extend='both')
plt.ylim(0.03, 1)
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Frequency [Hz]', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.suptitle('Wavelet analysis', fontsize=40)
plt.show()
In [49]:
from obspy import read
import matplotlib.pyplot as plt
st = read()[: 1]
st.filter('bandpass', freqmin=0.75, freqmax=10, zerophase=True)
st.plot(color='r', grid_color='#0000AA', linewidth=2, tick_format='relative', block=False, size=(1000, 250))
Out[49]:
In [102]:
from obspy import read
import matplotlib.pyplot as plt
import numpy as np
st = read()
tr = st[2]
tr.data = tr.data - np.mean(tr.data)
tr.detrend()
tr.taper(0.005)
tr.plot()
tr.spectrogram(log=False, cmap=plt.cm.jet)
In [16]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
from obspy.signal.tf_misfit import plot_tfr
st = read()
dt = st[0].stats.delta
f1 = 1
f2 = 5
for i, tr in enumerate(st):
st[i] = tr.filter('bandpass', freqmin=f1, freqmax=f2, zerophase=True)
plot_tfr(st[i].data, dt=dt, fmin=f1, fmax=f2, cmap='jet')
In [24]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
from obspy.signal.tf_misfit import plot_tf_misfits
st = read()
dt = st[0].stats.delta
f1 = 2
f2 = 8
for i, tr in enumerate(st):
st[i] = tr.filter('bandpass', freqmin=f1, freqmax=f2, zerophase=True)
plot_tf_misfits(st[0].data, st[1].data, dt=dt, fmin=f1, fmax=f2, cmap='jet', show=False)
Out[24]:
In [13]:
from obspy.clients.syngine import Client
import numpy as np
import matplotlib.pyplot as plt
models = ['ak135f_1s',
'ak135f_2s',
'ak135f_5s',
'iasp91_2s',
'prem_i_2s',
'prem_a_2s',
'prem_a_5s',
'prem_a_10s',
'prem_a_20s'
]
colors = ['b', 'r', 'g', 'k', 'm', 'y', 'c']
b = 'P-50'
e = 'S+500'
client = Client()
plt.figure(figsize=(15, 6))
for i, m in enumerate(models[: 4]):
print('model ' + str(i+1) + ': ', m )
st = client.get_waveforms(model=m, network="IU", station="ANMO",
eventid="GCMT:C201002270634A", starttime=b, endtime=e)
tr = st[0]
#tr.filter('bandpass', freqmin=0.01, freqmax=0.15, zerophase=True)
d = tr.data
t = np.arange(tr.stats.npts) * tr.stats.delta
plt.plot(t, d, linewidth=2, color=colors[i], label=m)
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize=20)
plt.show()
In [9]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
st = read()
tr = st[0]
tr.data = tr.data - np.mean(tr.data)
tr.detrend()
tr.taper(0.025)
tr.filter('bandpass', freqmin=0.5, freqmax=1.5, zerophase=True)
tr2 = tr.copy()
tr2.decimate(factor=10)
t = np.arange(len(tr.data)) * tr.stats.delta
t2 = np.arange(len(tr2.data)) * tr2.stats.delta
plt.figure(figsize=(15, 6))
plt.plot(t, tr.data, label='origin')
plt.plot(t2, tr2.data, label='downsampling')
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize=20)
plt.show()
In [9]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
st = read()
st[0].filter('bandpass', freqmin=0.5, freqmax=5, zerophase=True)
tr1 = st[0]
tr2 = st[1]
tr3 = st[2]
f11 = 1.0
f12 = 2.0
f21 = 1.2
f22 = 2.5
f31 = 2.0
f32 = 4.0
tr1.filter( 'bandpass', freqmin=f11, freqmax=f12, zerophase=True)
tr2.filter( 'bandpass', freqmin=f21, freqmax=f22, zerophase=True)
tr3.filter( 'bandpass', freqmin=f31, freqmax=f32, zerophase=True)
data1 = tr1.data
data2 = tr2.data
data3 = tr3.data
data1 = data1 / max(abs(data1))
data2 = data2 / max(abs(data2))
data3 = data3 / max(abs(data3))
t = np.linspace(0, tr1.stats.npts-1, tr1.stats.npts) * tr1.stats.delta
fig, ax = plt.subplots(figsize=(15, 9))
ax.plot( t, data1, 'g', linewidth=3, label=str(f11) + ' - ' + str(f12) + ' Hz' )
ax.text(1, 0.2, tr1.stats.channel, fontsize=20)
ax.plot( t, data2+2, 'r', linewidth=3, label=str(f21) + ' - ' + str(f22) + ' Hz' )
ax.text(1, 2.2, tr2.stats.channel, fontsize=18)
ax.plot( t, data3+4, 'b', linewidth=3, label=str(f31) + ' - ' + str(f32) + ' Hz' )
ax.text(1, 4.2, tr3.stats.channel, fontsize=18)
ax.set_xlabel( 'Time [s]', fontsize=18 )
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_yticks([])
ax.set_xticks(np.linspace(t[0], t[-1], 11))
plt.legend(fontsize=16, loc='best')
plt.xticks(fontsize=15)
plt.show()
In [72]:
from obspy import read
from obspy.signal.trigger import recursive_sta_lta, plot_trigger
import matplotlib.pyplot as plt
st = read()
tr = st.select(component="Z")[0]
tr.filter("bandpass", freqmin=1, freqmax=20)
sta = 0.5
lta = 4
cft = recursive_sta_lta(tr.data, int(sta * tr.stats.sampling_rate),
int(lta * tr.stats.sampling_rate))
thrOn = 2
thrOff = 0.5
plt.figure(figsize=(15, 9))
plot_trigger(tr, cft, thrOn, thrOff)
In [56]:
from obspy import read
from obspy.signal.trigger import recursive_sta_lta, plot_trigger
import matplotlib.pyplot as plt
st = read()
tr = st.select(component="Z")[0]
tr.trigger("recstalta", sta=0.1, lta=3)
tr.plot()
Out[56]:
In [49]:
import matplotlib.pyplot as plt
from obspy.imaging.beachball import beach
np1 = [150, 87, 1]
mt = [-2.39, 1.04, 1.35, 0.57, -2.94, -0.94]
b1 = beach(np1, facecolor='b', edgecolor='k', xy=(1, 1), width=1.25)
b2 = beach(np1, facecolor='r', edgecolor='k', xy=(3, 1), width=1.5)
b3 = beach(mt, facecolor='g', edgecolor='k', xy=(7, 1), width=1.75)
plt.figure(figsize=(15, 5))
plt.plot()
ax = plt.gca()
ax.add_collection(b1)
ax.add_collection(b2)
ax.add_collection(b3)
ax.set_aspect('equal')
ax.set_xlim(0, 10)
ax.set_ylim(-1, 3)
Out[49]:
In [29]:
import matplotlib.pyplot as plt
from obspy.imaging.beachball import beach
np1 = [150, 87, 1]
mt = [-2.39, 1.04, 1.35, 0.57, -2.94, -0.94]
beach1 = beach(np1, xy=(-70, 80), width=30)
beach2 = beach(mt, xy=(50, 50), width=50)
plt.figure(figsize=(15, 8))
plt.plot([-100, 100], [0, 100], "rv", ms=80)
ax = plt.gca()
ax.add_collection(beach1)
ax.add_collection(beach2)
ax.set_aspect("equal")
ax.set_xlim((-120, 120))
ax.set_ylim((-20, 120))
Out[29]:
In [17]:
from obspy.clients.iris import Client
client = Client()
result = client.traveltime(model='prem',
evloc=(-36.122,-72.898),
staloc=[(-33.45,-70.67), (47.61,-122.33), (35.69,139.69)],
evdepth=22.9,
phases = ['P', 'S', 'pP', 'sS'])
result = result.decode()
print(result)
In [19]:
from obspy.taup import plot_travel_times
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 7))
ax = plot_travel_times(source_depth=10, ax=ax, fig=fig,
phase_list=['P', 'PP', 'S'], npoints=200)
In [62]:
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
phase_list=['PP', 'SSS', 'SKS', 'PKP', 'SKKS', 'PKKP', 'SKIKS', 'PKJKP', 'SKJKS', 'PKIKP', 'PKIIKP']
model = TauPyModel(model='iasp91')
arrivals = model.get_ray_paths(500, 140, phase_list=phase_list)
fig, ax = plt.subplots(figsize=(12, 8))
ax.xaxis.set_ticks_position('top')
ax.invert_yaxis()
ax = arrivals.plot_rays(plot_type='cartesian', phase_list=phase_list,
plot_all=False, legend=True, ax = ax)
In [38]:
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
model = TauPyModel(model='iasp91')
arrivals = model.get_ray_paths(500, 140, phase_list=['Pdiff', 'SS'])
fig, ax = plt.subplots(figsize=(12, 10))
arrivals.plot_rays(plot_type='spherical', phase_list=['Pdiff', 'SS'],
legend=True, fig=fig)
Out[38]:
In [39]:
from obspy.taup.tau import plot_ray_paths
import matplotlib.pyplot as plt
fig, ax = plt.subplots(subplot_kw=dict(polar=True), figsize=(10, 9))
ax = plot_ray_paths(source_depth=100, ax=ax, fig=fig, phase_list=['P', 'PKP'],
npoints=25)
In [41]:
import numpy as np
import matplotlib.pyplot as plt
from obspy.taup import TauPyModel
PHASES = [
# Phase, distance
('P', 26),
('PP', 60),
('PPP', 94),
('PPS', 155),
('p', 3),
('pPcP', 100),
('PKIKP', 170),
('PKJKP', 194),
('S', 65),
('SP', 85),
('SS', 134.5),
('SSS', 204),
('p', -10),
('pP', -37.5),
('s', -3),
('sP', -49),
('ScS', -44),
('SKS', -82),
('SKKS', -120),
]
model = TauPyModel(model='iasp91')
fig, ax = plt.subplots(subplot_kw=dict(polar=True), figsize=(10, 10))
# Plot all pre-determined phases
for phase, distance in PHASES:
arrivals = model.get_ray_paths(700, distance, phase_list=[phase])
ax = arrivals.plot_rays(plot_type='spherical',
legend=False, label_arrivals=True,
plot_all=True,
show=False, ax=ax)
# Annotate regions
ax.text(0, 0, 'Solid\ninner\ncore',
horizontalalignment='center', verticalalignment='center',
bbox=dict(facecolor='white', edgecolor='none', alpha=0.7))
ocr = (model.model.radius_of_planet -
(model.model.s_mod.v_mod.iocb_depth +
model.model.s_mod.v_mod.cmb_depth) / 2)
ax.text(np.deg2rad(180), ocr, 'Fluid outer core',
horizontalalignment='center',
bbox=dict(facecolor='white', edgecolor='none', alpha=0.7))
mr = model.model.radius_of_planet - model.model.s_mod.v_mod.cmb_depth / 2
ax.text(np.deg2rad(180), mr, 'Solid mantle',
horizontalalignment='center',
bbox=dict(facecolor='white', edgecolor='none', alpha=0.7))
plt.show()
In [42]:
from __future__ import print_function
import obspy
import obspy.clients.fdsn
client = obspy.clients.fdsn.Client("EMSC")
events = client.get_events(minlatitude=46.1, maxlatitude=46.3,
minlongitude=7.6, maxlongitude=7.8,
starttime=obspy.UTCDateTime("2012-04-03"),
endtime=obspy.UTCDateTime("2012-04-04"))
print("found %s event(s):" % len(events))
for event in events:
print(event)
In [14]:
from obspy.taup.taup_time import TauPTime as TPT
model = TPT(model='prem', phase_list=['P', 'S'], depth=50, degrees=60, receiver_depth=0)
print(model.source_depth)
In [ ]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
st = read('1*.sac')
tr = st[3]
dt = tr.stats.delta
fs = 1 / dt
n = tr.stats.npts
print(dt)
tr.detrend()
tr.data = tr.data - np.mean(tr.data)
tr.taper(0.025)
tr.filter('bandpass', freqmin=0.05, freqmax=0.1, corners=4, zerophase=True)
t = np.arange(n) * dt + tr.stats.sac.b
s = tr.data
s = s /max(abs(s))
fft_s = abs(np.fft.fft(s))
f = np.arange(n) * fs / n
fft_s = fft_s[0: n//2]
f = f[0: n//2]
plt.figure(figsize=(20, 10))
plt.subplot(211)
plt.plot(t, s, 'k', linewidth=2)
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.subplot(212)
plt.stem(f, fft_s, 'b', linewidth=2)
plt.xlabel('Frequency [Hz]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.show()
In [5]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
from obspy.signal.filter import envelope
st = read('STA*.SAC')
tr = st[0]
n0 = 100000
tr.data = tr.data[: n0]
tr.detrend()
tr.data = tr.data - np.mean(tr.data)
tr.taper(0.05)
f1 = 0.2
f2 = 1.5
tr.filter('bandpass', freqmin=f1, freqmax=f2, zerophase=True)
s = tr.data
tr.filter('bandpass', freqmin=f1, freqmax=f2*0.75, zerophase=True)
s2 = tr.data
s3 = envelope(s2)
t = np.arange(len(s)) * tr.stats.delta + tr.stats.sac.b
f = np.arange(n0) / tr.stats.delta / n0
fft_s = abs(np.fft.fft(s))
f = f[0: n0//2]
fft_s = fft_s[0: n0//2]
plt.figure(figsize=(18, 10))
plt.subplot(211)
plt.plot(t, s, 'r', linewidth=1.5, label='filtered 1')
plt.plot(t, s2, 'b', linewidth=1.5, label='filtered 2')
plt.plot(t, s3, 'g', linewidth=3, label='envelope')
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.legend(fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.subplot(212)
plt.plot(f, fft_s, 'b', linewidth=1.5)
plt.xlabel('Frequency [Hz]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xlim(f1-f1*0.5, f2+f1*0.5)
plt.show()
In [30]:
import numpy as np
from obspy import read
from obspy.signal import PPSD
import matplotlib.pyplot as plt
st = read('1*.sac')
tr = st[0]
paz = {'gain': 60077000.0,
'poles': [-0.037004+0.037016j, -0.037004-0.037016j,
-251.33+0j, -131.04-467.29j, -131.04+467.29j],
'sensitivity': 2516778400.0,
'zeros': [0j, 0j]}
ppsd = PPSD(tr.stats, paz)
ppsd.add(st)
plt.figure(figsize=(15, 10))
ppsd.plot(cmap=plt.cm.jet)
In [35]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp2d as interp2d
st = read('Australis_data\Data\*BHZ*.SAC')
nr = 20
st = st[: nr]
t1 = 800
t2 = 3000
dt = st[0].stats.delta
n1 = int(t1/dt)
n2 = int(t2/dt)
tt = np.arange(n2-n1) * dt + t1
plt.figure(figsize=(20, 10))
for i, tr in enumerate(st):
dt = tr.stats.delta
d = tr.data[n1: n2]
tr.data = d - np.mean(d)
tr.detrend()
tr.taper(0.0025)
tr.filter('bandpass', freqmin=0.005, freqmax=0.05, corners=4, zerophase=True)
tr.data = tr.data / max(abs(tr.data)) * 1.75
n = len(tr.data)
t = np.arange(n) * dt + t1
d = tr.data
d = d / max(abs(d)) * 5
plt.plot(t, d+tr.stats.sac.gcarc, color='r', alpha=0.25, linewidth=2)
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Distance [$^o$]', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# Travel times of some phases
phase1 = dict()
phase2 = dict()
colors = ['#FF0000', '#00FF00', '#0000FF', '#00FFFF', '#000000', '#FFFF00', '#FF00FF', '#999999', '#FFA500', '#800080']
for i, tr in enumerate(st):
n = len(tr.data)
t = np.arange(n) * dt + t1
phase1['0'] = tr.stats.sac.t0
phase1['1'] = tr.stats.sac.t1
phase1['2'] = tr.stats.sac.t2
phase1['3'] = tr.stats.sac.t3
phase1['4'] = tr.stats.sac.t4
phase1['5'] = tr.stats.sac.t5
phase1['6'] = tr.stats.sac.t6
phase1['7'] = tr.stats.sac.t7
phase1['8'] = tr.stats.sac.t8
phase1['9'] = tr.stats.sac.t9
phase2['0'] = tr.stats.sac.kt0
phase2['1'] = tr.stats.sac.kt1
phase2['2'] = tr.stats.sac.kt2
phase2['3'] = tr.stats.sac.kt3
phase2['4'] = tr.stats.sac.kt4
phase2['5'] = tr.stats.sac.kt5
phase2['6'] = tr.stats.sac.kt6
phase2['7'] = tr.stats.sac.kt7
phase2['8'] = tr.stats.sac.kt8
phase2['9'] = tr.stats.sac.kt9
if i < 1:
index = 0
for (k1, k2) in zip(phase1.keys(), phase2.keys()):
plt.plot(phase1[k1], tr.stats.sac.gcarc, 'o', color=colors[index], linewidth=1, label=phase2[k2])
index += 1
else:
index = 0
for k in phase1.keys():
plt.plot(phase1[k], tr.stats.sac.gcarc, 'o', color=colors[index], linewidth=1)
index += 1
plt.legend(loc='best', fontsize=13)
plt.xlim(t1, t2)
plt.show()
In [315]:
from obspy import read
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp2d as interp2d
st = read('Australis_data\Data\*BHZ*.SAC')
nr = 20
st = st[: nr]
t1 = 800
t2 = 1600
dt = st[0].stats.delta
n1 = int(t1/dt)
n2 = int(t2/dt)
tt = np.arange(n2-n1) * dt + t1
seis = np.zeros((nr, (n2-n1)))
dist = []
for i, tr in enumerate(st):
dt = tr.stats.delta
dist.append(tr.stats.sac.gcarc)
d = tr.data[n1: n2]
tr.data = d - np.mean(d)
tr.detrend()
tr.taper(0.0025)
tr.filter('bandpass', freqmin=0.005, freqmax=0.05, corners=4, zerophase=True)
tr.data = tr.data / max(abs(tr.data)) * 1.75
n = len(tr.data)
t = np.arange(n) * dt + t1
seis[i] = tr.data
plt.figure(figsize=(15, 12))
T, D = np.meshgrid(tt, dist)
plt.scatter(T, D, c=seis, marker='s', s=500, cmap='RdGy_r', edgecolor='None')
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Distance [$^o$]', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
for i, tr in enumerate(st):
n = len(tr.data)
t = np.arange(n) * dt + t1
#plt.plot(t, seis[i]+tr.stats.sac.gcarc, color='#ECCBAF', linewidth=2)
plt.plot(t, seis[i]+tr.stats.sac.gcarc, color='g', linewidth=2)
if i < 1:
plt.plot(tr.stats.sac.t3, tr.stats.sac.gcarc, 'ro', linewidth=1, label=tr.stats.sac.kt3)
plt.plot(tr.stats.sac.t4, tr.stats.sac.gcarc, 'bo', linewidth=1, label=tr.stats.sac.kt4)
plt.plot(tr.stats.sac.t5, tr.stats.sac.gcarc, 'go', linewidth=1, label=tr.stats.sac.kt5)
else:
plt.plot(tr.stats.sac.t3, tr.stats.sac.gcarc, 'ro', linewidth=1)
plt.plot(tr.stats.sac.t4, tr.stats.sac.gcarc, 'bo', linewidth=1)
plt.plot(tr.stats.sac.t5, tr.stats.sac.gcarc, 'go', linewidth=1)
plt.legend(loc='best', fontsize=13)
plt.xlim(t1, t2)
plt.show()
In [279]:
from obspy.taup import TauPyModel as TP
import numpy as np
import matplotlib.pyplot as plt
n1 = 25
n2 = 25
times = np.zeros((n1, n2))
dist = np.arange(n1) * 1 + 130
depth = np.arange(n2) * 2 + 20
tp = TP('prem')
for i, d1 in enumerate(dist):
for j, d2 in enumerate(depth):
arr = tp.get_travel_times(source_depth_in_km=d2, distance_in_degree=d1, phase_list=['PKIKP', 'PKIIKP'])
times[i, j] = arr[1].time
#print(d1, d2, times[i, j])
plt.figure(figsize=(15, 12))
plt.subplot(211)
#plt.plot(times, dist, 'ro', linewidth=2)
#plt.xlabel('Travel times [s]', fontsize=25)
#plt.ylabel('$ Distance [^o]$', fontsize=25)
plt.pcolormesh(depth, dist, times, cmap='RdGy')
plt.ylabel('Distance [deg]', fontsize=25)
plt.xlabel('Depth [km]', fontsize=25)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.colorbar()
plt.subplot(212)
plt.plot(depth, times[0], 'ko', linewidth=2)
plt.ylabel('Time [s]', fontsize=25)
plt.xlabel('Depth [km]', fontsize=25)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.show()
In [11]:
import numpy as np
import matplotlib.pyplot as plt
from obspy.imaging.cm import obspy_sequential
from obspy.signal.array_analysis import array_transff_wavenumber
# generate array coordinates
r = 300
coords1 = np.array([[0., r, 0.],
[r*np.sqrt(3)/2, r/2., 0.],
[r*np.sqrt(3)/2, -r/2., 0.],
[0., -r, 0.],
[-r*np.sqrt(3)/2, -r/2, 0.],
[-r*np.sqrt(3)/2, r/2, 0],
])
coords2 = r * np.array([[-3, 0, 0],
[-1.5, 0, 0],
[0, 0, 0],
[1.5, 0, 0],
[3, 0, 0],
[0, -3, 0],
[0, -1.5, 0],
[0, 1.5, 0],
[0, 3, 0]]) / 3
n3 = 10
theta = np.linspace(-180, 180, n3) / 180 * np.pi
coords3 = np.zeros((n3, 3))
coords3[:, 0] = np.cos(theta) * r
coords3[:, 1] = np.sin(theta) * r
n4 = 21
coords4 = np.zeros((n4, 3))
coords4[:, 0] = np.linspace(-r, r, n4)
coords4[:, 1] = np.cos(coords4[:, 0]/r*4) * r
# coordinates in km
coords1 /= 1000
coords2 /= 1000
coords3 /= 1000
coords4 /= 1000
# set limits for wavenumber differences to analyze
klim = 100.
kxmin = -klim
kxmax = klim
kymin = -klim
kymax = klim
kstep = klim / 50.
# compute transfer function as a function of wavenumber difference
transff1 = array_transff_wavenumber(coords1, klim, kstep, coordsys='xy')
transff2 = array_transff_wavenumber(coords2, klim, kstep, coordsys='xy')
transff3 = array_transff_wavenumber(coords3, klim, kstep, coordsys='xy')
transff4 = array_transff_wavenumber(coords4, klim, kstep, coordsys='xy')
# Plot array1
plt.figure(figsize=(24, 10))
# Array map
plt.subplot(241)
plt.scatter(coords1[:, 0], coords1[:, 1], marker='v', s=200, facecolor='b')
plt.xlabel('X [km]', fontsize=15)
plt.ylabel('Y [km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.subplot(242)
plt.scatter(coords2[:, 0], coords2[:, 1], marker='v', s=200, facecolor='b')
plt.xlabel('X [km]', fontsize=15)
plt.ylabel('Y [km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.subplot(243)
plt.scatter(coords3[:, 0], coords3[:, 1], marker='v', s=200, facecolor='b')
plt.xlabel('X [km]', fontsize=15)
plt.ylabel('Y [km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.subplot(244)
plt.scatter(coords4[:, 0], coords4[:, 1], marker='v', s=200, facecolor='b')
plt.xlabel('X [km]', fontsize=15)
plt.ylabel('Y [km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# Array Transfer Function
plt.subplot(245)
plt.pcolor(np.arange(kxmin, kxmax + kstep * 1.1, kstep) - kstep / 2.,
np.arange(kymin, kymax + kstep * 1.1, kstep) - kstep / 2.,
transff1.T, cmap='jet')
plt.colorbar()
plt.xlabel('Slowness [s/km]', fontsize=15)
plt.ylabel('Slowness [s/km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# Plot array2
plt.subplot(246)
plt.pcolor(np.arange(kxmin, kxmax + kstep * 1.1, kstep) - kstep / 2.,
np.arange(kymin, kymax + kstep * 1.1, kstep) - kstep / 2.,
transff2.T, cmap='viridis')
plt.colorbar()
plt.xlabel('Slowness [s/km]', fontsize=15)
plt.ylabel('Slowness [s/km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# Plot array3
plt.subplot(247)
plt.pcolor(np.arange(kxmin, kxmax + kstep * 1.1, kstep) - kstep / 2.,
np.arange(kymin, kymax + kstep * 1.1, kstep) - kstep / 2.,
transff3.T, cmap='hot')
plt.colorbar()
plt.xlabel('Slowness [s/km]', fontsize=15)
plt.ylabel('Slowness [s/km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# Plot array4
plt.subplot(248)
plt.pcolor(np.arange(kxmin, kxmax + kstep * 1.1, kstep) - kstep / 2.,
np.arange(kymin, kymax + kstep * 1.1, kstep) - kstep / 2.,
transff4.T, cmap='bone')
plt.colorbar()
plt.xlabel('Slowness [s/km]', fontsize=15)
plt.ylabel('Slowness [s/km]', fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.suptitle('Array Transfer Function', fontsize=25)
plt.show()
In [ ]:
from obspy.clients.syngine import Client
client = Client()
st = client.get_waveforms(model="ak135f_5s", network="IU", station="ANMO",
eventid="GCMT:C201002270634A")
print(st)
st[0].plot()
In [90]:
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
b = UTCDateTime('2019-08-05T00:00:00.000')
e = UTCDateTime('2599-12-31T23:59:59.999')
clint = Client('IRIS')
sta = clint.get_stations(minlongitude=120,
maxlongitude=130,
minlatitude=20,
maxlatitude=50,
station='*',
starttime=b,
endtime=e)
print(sta)
stations = sta.get_contents()['stations']
for s in stations[: 10]:
sn = s.split()[0]
print(sn)
In [309]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
nf = 20
shift = np.random.uniform(-1, 1, nf) * np.pi
ss = np.zeros((nf, 1001))
for i in range(nf):
ss[i] = np.sin((i+1)*2*np.pi*t+shift[i]) * np.random.rand(1001)
s = sum(ss)
#s = s1
fft_s = abs(np.fft.fft(s))
f = np.arange(1001) * 100 / 1001
fft_s = fft_s[: 500]
f = f[: 500]
plt.figure(figsize=(12, 10))
plt.subplot(311)
plt.plot(t, s, linewidth=2)
plt.subplot(312)
plt.plot(f, fft_s)
plt.subplot(313)
plt.plot(np.arange(nf), shift)
plt.plot(np.arange(nf), shift, 'ko')
plt.show()
In [19]:
import numpy as np
import matplotlib.pyplot as plt
n = 21
x = np.linspace(0, 50, n)
y = np.linspace(0, 50, n)
X, Y = np.meshgrid(x, y)
s = np.sin(X*X)*np.cos(Y) + np.cos(Y)*np.sin(X) + np.random.random((n, n)) + np.random.random((n, n)) + \
np.random.random((n, n)) +np.random.random((n, n)) + np.random.random((n, n)) + np.random.random((n, n))
plt.figure(figsize=(15,10))
plt.scatter(X, Y, c=s, marker='s',linewidth=1, s=300, alpha=1, cmap='viridis', edgecolor='None')
plt.xlim(-5,55)
plt.ylim(-5,55)
#plt.contourf(X, Y, s, cmap='viridis')
plt.colorbar()
plt.show()
In [22]:
import numpy as np
import matplotlib.pyplot as plt
n = 21
x = np.linspace(0, 50, n)
y = np.linspace(0, 50, n)
X, Y = np.meshgrid(x, y)
s = np.sin(X*X)*np.cos(Y) + np.cos(Y)*np.sin(X) + np.random.random((n, n)) + np.random.random((n, n)) +\
np.random.random((n, n)) +np.random.random((n, n)) + np.random.random((n, n)) + np.random.random((n, n))
#plt.scatter(X, Y, c=s, marker='s',linewidth=1, s=120, alpha=1, cmap='viridis', edgecolor='None')
plt.figure(figsize=(15,10))
plt.xlim(-5,55)
plt.ylim(-5,55)
plt.contourf(X, Y, s, cmap='viridis')
plt.colorbar()
plt.title('contourf', fontsize=20)
plt.show()
In [24]:
import numpy as np
import matplotlib.pyplot as plt
arr = np.random.random((10, 10))
plt.figure(figsize=(18,10))
fig, axs = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True)
for ax in axs.flatten():
im = ax.pcolormesh(arr,cmap='viridis')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
fig.colorbar(im, ax=axs, shrink=0.75, extend='both')
plt.suptitle('plt.pcolormesh', fontsize=25)
plt.show()
In [25]:
import numpy as np
import matplotlib.pyplot as plt
arr = np.random.random((100, 100))
x = np.linspace(-5,5, 51)
y = np.linspace(-3, 3, 61)
X, Y = np.meshgrid(x, y)
v = np.exp(-X**2-Y**2)
r = np.sqrt(X**2+Y**2)
plt.figure(figsize=(20, 5))
plt.subplot(131)
plt.pcolormesh(arr, cmap='RdBu_r')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.colorbar()
plt.subplot(132)
plt.pcolormesh(X, Y, v, cmap='RdBu_r')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.colorbar(extend='both')
plt.subplot(133)
plt.pcolormesh(X, Y, r, cmap='seismic')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.colorbar(extend='both')
plt.suptitle('pcolormesh different colorbars', fontsize=30)
plt.show()
In [32]:
import numpy as np
import matplotlib.pyplot as plt
n = 41
x = np.linspace(-10, 10, n)
y = np.linspace(-10, 10, n)
X, Y = np.meshgrid(x, y)
v = np.sqrt(X**2+Y**2)
plt.figure(figsize=(10,8))
plt.pcolormesh(X, Y, v, cmap='hot')
plt.xlabel('X axis', fontsize=20)
plt.ylabel('Y axis', fontsize=20)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
cbar = plt.colorbar(extend='both', shrink=0.75)
cbar.set_label('Power', fontsize=20)
cbar.set_ticks(np.arange(0, 15, 2))
plt.title('Set label of colorbar', fontsize=20)
plt.show()
In [36]:
import numpy as np
import matplotlib.pyplot as plt
n = 21
x = np.linspace(0, 50, n)
y = np.linspace(0, 50, n)
X, Y = np.meshgrid(x, y)
s = np.sin(X*X)*np.cos(Y) + np.cos(Y)*np.sin(X) + np.random.random((n, n)) + np.random.random((n, n)) + \
np.random.random((n, n)) +np.random.random((n, n)) + np.random.random((n, n)) + np.random.random((n, n))
plt.figure(figsize=(10, 8))
plt.scatter(X, Y, c=s, marker='o',linewidth=1, s=abs(s)*100, alpha=1, cmap='seismic', edgecolor='None')
plt.xlim(-5,55)
plt.ylim(-5,55)
#plt.contourf(X, Y, s, cmap='viridis')
plt.colorbar(extend='both', shrink=0.75)
plt.title('different sizes of scatters', fontsize=20)
plt.show()
In [38]:
import numpy as np
import matplotlib.pyplot as plt
n = 6
x1 = np.linspace(0, 50, n)
x2 = np.linspace(1, 51, n)
y1 = np.linspace(0, 50, n)
y2 = np.linspace(1, 51, n)
X1, Y1 = np.meshgrid(x1, y1)
X2, Y2 = np.meshgrid(x2, y2)
s1 = np.sin(X1*X1)*np.cos(Y1) + np.cos(Y1)*np.sin(X1) + np.random.random((n,n))
s2 = np.random.random((n, n))
plt.figure(figsize=(10, 8))
plt.subplot(111)
plt.scatter(X1, Y1, c=s1, marker='s',linewidth=1, s=500, alpha=1, cmap='viridis', edgecolor='None', label='squares')
plt.colorbar(extend='both', shrink=0.75)
plt.subplot(111)
plt.scatter(X2, Y2, c=s2, marker='o',linewidth=1, s=500, alpha=1, cmap='hot', edgecolor='None',label='circles')
plt.xlim(-5,55)
plt.ylim(-5,55)
plt.colorbar(extend='both', shrink=0.75)
plt.legend(loc='best')
plt.show()
In [41]:
import numpy as np
import matplotlib.pyplot as plt
na = 37
nr = 7
azimuths = np.radians(np.linspace(0, 360, na))
zeniths = np.linspace(0, 10, nr)
r, theta = np.meshgrid(zeniths, azimuths)
values = np.random.random((na, nr))
fig, ax = plt.subplots(subplot_kw=dict(projection='polar'), figsize=(10, 8))
p = ax.contourf(theta, r, values, cmap='plasma')
ax.set_title('Polar contourf plot', fontsize=25)
cbar = plt.colorbar(p, extend='both', shrink=0.75)
plt.show()
In [45]:
import numpy as np
import matplotlib.pyplot as plt
azimuths = np.radians(np.linspace(0, 360, 37))
#zeniths = np.arange(0, 11, 10)
zeniths = np.linspace(0, 10, 2)
r, theta = np.meshgrid(zeniths, azimuths)
values1 = np.random.random((azimuths.size, zeniths.size))
values2 = np.random.random((azimuths.size, zeniths.size))
fig, axs = plt.subplots(1, 2, subplot_kw=dict(projection='polar'), figsize=(12, 9))
p1 = axs[0].contourf(theta, r, values1, 100, cmap='jet')
plt.colorbar(p1, ax=axs[1])
p2 = axs[1].contourf(theta, r, values2, 100, cmap='viridis')
plt.colorbar(p2, ax=axs[1])
plt.suptitle('Polar contourf', fontsize=20)
plt.show()
In [87]:
import numpy as np
import matplotlib.pyplot as plt
theta = np.arange(0, 2*np.pi, np.pi/360)
plt.figure(figsize=(15, 6))
plt.subplot(121, polar=True)
plt.plot(theta, 1.4*np.cos(5*theta), 'b--', linewidth=2)
plt.plot(theta, 1.8*np.cos(4*theta), 'g', linewidth=2)
plt.rgrids(np.linspace(0.5, 2.0, 4), angle=45, color='r', fontsize=15)
plt.thetagrids([0, 90, 180, 270], fontsize=15)
plt.subplot(122, polar=True)
plt.plot(theta, 1.6*np.ones_like(theta), 'b', linewidth=2) #绘制同心圆
plt.plot(3*theta, theta/3, 'g--', linewidth=2)
plt.rgrids(np.linspace(0.5, 2.0, 4), angle=45, color='r', fontsize=15)
plt.thetagrids([0, 90, 180, 270], fontsize=15)
plt.show()
In [337]:
import numpy as np
import matplotlib.pyplot as plt
n = 360
theta = np.arange(n) * np.pi / 18
rho = np.random.rand(n) + 0.5
area = 300 * rho ** 2
plt.figure(figsize=(20, 7))
plt.subplot(131, projection='polar')
plt.scatter(theta, rho, c=theta, s=area, cmap='jet', alpha=0.75)
plt.title('poloar 1')
ax = plt.subplot(132, projection='polar')
ax.scatter(theta, rho, c=theta, s=area, cmap='RdGy_r', alpha=0.75)
ax.set_thetamin(90)
ax.set_thetamax(180)
plt.title('poloar 2')
ax = plt.subplot(133, projection='polar')
ax.scatter(theta, rho, c=theta, s=area, cmap='viridis', alpha=0.75)
ax.set_rorigin(-0.75)
ax.set_theta_zero_location('W', offset=10)
plt.title('poloar 3')
plt.show()
In [349]:
import numpy as np
import matplotlib.pyplot as plt
n = 360
theta = np.arange(n) * np.pi / 18
rho = np.random.rand(n) + 0.5
area = 300 * rho ** 2
plt.figure(figsize=(20, 7))
plt.subplot(141, projection='polar')
plt.scatter(theta, rho, c=theta, s=area, cmap='hot', alpha=0.75, marker=r'$\clubsuit$')
plt.title('poloar 1')
ax = plt.subplot(142, projection='polar')
ax.scatter(theta, rho, c=theta, s=area, cmap='jet', alpha=0.75, marker=r'$\spadesuit$')
ax.set_thetamin(90)
ax.set_thetamax(180)
plt.title('poloar 2')
ax = plt.subplot(143, projection='polar')
ax.scatter(theta, rho, c=theta, s=area, cmap='plasma', alpha=0.75, marker=r'$\heartsuit$')
ax.set_rorigin(-0.75)
ax.set_theta_zero_location('W', offset=10)
plt.title('poloar 3')
ax = plt.subplot(144, projection='polar')
ax.scatter(theta, rho, c=theta, s=area, cmap='Spectral', alpha=0.75, marker=r'$\diamondsuit$')
ax.set_rorigin(-0.75)
ax.set_theta_zero_location('W', offset=10)
plt.title('poloar 3')
plt.show()
In [83]:
import numpy as np
import matplotlib.pyplot as plt
n = 8
X, Y = np.mgrid[0:n, 0:n]
T = np.arctan2(Y - n / 2., X - n/2.)
R = 10 + np.sqrt((Y - n / 2.0) ** 2 + (X - n / 2.0) ** 2)
U, V = R * np.cos(T), R * np.sin(T)
plt.figure(figsize=(22, 6))
plt.subplot(131)
plt.quiver(X, Y, U, V, R, alpha=.5, cmap='jet')
plt.xlim(-1, n)
plt.xticks(())
plt.ylim(-1, n)
plt.yticks(())
plt.subplot(132)
plt.quiver(X, Y, U, V, R, edgecolor='k', linewidth=2, cmap='viridis')
plt.xlim(-1, n)
plt.xticks(())
plt.ylim(-1, n)
plt.yticks(())
plt.subplot(133)
plt.quiver(X, Y, U*10, V, edgecolor='k', facecolor='None', linewidth=1)
plt.xlim(-1, n)
plt.xticks(())
plt.ylim(-1, n)
plt.yticks(())
plt.suptitle('Flow arrows', fontsize=28)
plt.show()
In [89]:
import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(10, 6))
ax = plt.subplot(111)
t = np.linspace(0, 5, 501)
s = np.cos(2*np.pi*t)
line, = plt.plot(t, s,'k', lw=4)
plt.annotate('local max', xy=(2, 1), xytext=(4, 1.5), arrowprops=dict(edgecolor='b', facecolor=
'red', shrink=0.05), fontsize=15)
plt.ylim(-2, 2)
plt.title('Arrow marks', fontsize=25)
plt.show()
In [95]:
import numpy as np
import matplotlib.pyplot as plt
with plt.style.context(('dark_background')):
plt.figure(figsize=(10,6))
plt.plot(np.sin(np.linspace(0, 4*np.pi, 101)**2)*np.exp(-(np.linspace(0, 4*np.pi, 101))), 'r-^')
plt.title('black background', fontsize=25)
plt.grid(True)
plt.show()
In [354]:
import numpy as np
import matplotlib.pyplot as plt
category_names = ['Strongly disagree', 'Disagree',
'Neither agree nor disagree', 'Agree', 'Strongly agree']
results = {
'Question 1': [10, 15, 17, 32, 26],
'Question 2': [26, 22, 29, 10, 13],
'Question 3': [35, 37, 7, 2, 19],
'Question 4': [32, 11, 9, 15, 33],
'Question 5': [21, 29, 5, 5, 40],
'Question 6': [8, 19, 5, 30, 38]
}
def survey(results, category_names):
"""
Parameters
----------
results : dict
A mapping from question labels to a list of answers per category.
It is assumed all lists contain the same number of entries and that
it matches the length of *category_names*.
category_names : list of str
The category labels.
"""
labels = list(results.keys())
data = np.array(list(results.values()))
data_cum = data.cumsum(axis=1)
category_colors = plt.get_cmap('RdGy_r')(
np.linspace(0.15, 0.85, data.shape[1]))
fig, ax = plt.subplots(figsize=(15, 7))
ax.invert_yaxis()
ax.xaxis.set_visible(False)
ax.set_xlim(0, np.sum(data, axis=1).max())
for i, (colname, color) in enumerate(zip(category_names, category_colors)):
widths = data[:, i]
starts = data_cum[:, i] - widths
ax.barh(labels, widths, left=starts, height=0.5,
label=colname, color=color)
xcenters = starts + widths / 2
r, g, b, _ = color
text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
for y, (x, c) in enumerate(zip(xcenters, widths)):
ax.text(x, y, str(int(c)), ha='center', va='center',
color=text_color)
ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),
loc='lower left', fontsize='large')
return fig, ax
survey(results, category_names)
Out[354]:
In [359]:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(14)
y = np.sin(x / 2)
plt.figure(figsize=(15, 10))
plt.step(x, y + 2, label='pre (default)')
plt.plot(x, y + 2, 'C0o', alpha=0.5)
plt.step(x, y + 1, where='mid', label='mid')
plt.plot(x, y + 1, 'C1o', alpha=0.5)
plt.step(x, y, where='post', label='post')
plt.plot(x, y, 'C2o', alpha=0.5)
plt.legend(title='Parameter where:')
plt.show()
In [372]:
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 5))
plt.subplot(221, projection='aitoff')
plt.title('aitoff')
plt.subplot(222, projection='hammer')
plt.title('hammer')
plt.grid(True)
plt.subplot(223, projection='lambert')
plt.title('lambert')
plt.grid(True)
plt.subplot(224, projection='mollweide')
plt.title('mollweide')
plt.grid(True)
plt.show()
In [383]:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
w = 3
Y, X = np.mgrid[-w:w:100j, -w:w:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U**2 + V**2)
fig = plt.figure(figsize=(10, 13))
gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])
# Varying density along a streamline
ax0 = fig.add_subplot(gs[0, 0])
ax0.streamplot(X, Y, U, V, density=[0.5, 1])
ax0.set_title('Varying Density')
ax1 = fig.add_subplot(gs[0, 1])
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='jet')
fig.colorbar(strm.lines)
ax1.set_title('Varying Color')
# Varying line width along a streamline
ax2 = fig.add_subplot(gs[1, 0])
lw = 5*speed / speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
ax2.set_title('Varying Line Width')
# Controlling the starting points of the streamlines
seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1, 0, 1, 2, 2]])
ax3 = fig.add_subplot(gs[1, 1])
strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,
cmap='autumn', start_points=seed_points.T)
fig.colorbar(strm.lines)
ax3.set_title('Controlling Starting Points')
# Displaying the starting points with blue symbols.
ax3.plot(seed_points[0], seed_points[1], 'bo')
ax3.set(xlim=(-w, w), ylim=(-w, w))
# Create a mask
mask = np.zeros(U.shape, dtype=bool)
mask[40:60, 40:60] = True
U[:20, :20] = np.nan
U = np.ma.array(U, mask=mask)
ax4 = fig.add_subplot(gs[2, 0])
ax4.streamplot(X, Y, U, V, color='r')
ax4.set_title('Streamplot with Masking')
ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
interpolation='nearest', cmap='gray', aspect='auto')
ax4.set_aspect('equal')
plt.tight_layout()
plt.show()
In [412]:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
n = 20
Z = np.arange(n**2).reshape((n, n))
Z[:, 10:] = 1
fig=plt.figure()
plt.imshow(Z)
plt.imshow(Z)
plt.show()
In [392]:
import matplotlib.pyplot as plt
import numpy as np
def func3(x, y):
return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2 + y**2))
# make these smaller to increase the resolution
dx, dy = 0.05, 0.05
x = np.arange(-3.0, 3.0, dx)
y = np.arange(-3.0, 3.0, dy)
X, Y = np.meshgrid(x, y)
extent = np.min(x), np.max(x), np.min(y), np.max(y)
fig = plt.figure(frameon=False, figsize=(10, 8))
Z1 = np.add.outer(range(8), range(8)) % 2 # chessboard
im1 = plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',
extent=extent)
Z2 = func3(X, Y)
im2 = plt.imshow(Z2, cmap=plt.cm.jet, alpha=.9, interpolation='bilinear',
extent=extent)
plt.show()
In [394]:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes
def get_demo_image():
# prepare image
delta = 0.5
extent = (-3, 4, -4, 3)
x = np.arange(-3.0, 4.001, delta)
y = np.arange(-4.0, 3.001, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
return Z, extent
def get_rgb():
Z, extent = get_demo_image()
Z[Z < 0] = 0.
Z = Z / Z.max()
R = Z[:13, :13]
G = Z[2:, 2:]
B = Z[:13, 2:]
return R, G, B
fig = plt.figure(figsize=(10, 7))
ax = RGBAxes(fig, [0.1, 0.1, 0.8, 0.8])
r, g, b = get_rgb()
kwargs = dict(origin="lower", interpolation="nearest")
ax.imshow_rgb(r, g, b, **kwargs)
ax.RGB.set_xlim(0., 9.5)
ax.RGB.set_ylim(0.9, 10.6)
plt.show()
In [423]:
import numpy as np
import matplotlib.pyplot as plt
def koch_snowflake(order, scale=10):
def _koch_snowflake_complex(order):
if order == 0:
# initial triangle
angles = np.array([0, 120, 240]) + 90
return scale / np.sqrt(3) * np.exp(np.deg2rad(angles) * 1j)
else:
ZR = 0.5 - 0.5j * np.sqrt(3) / 3
p1 = _koch_snowflake_complex(order - 1) # start points
p2 = np.roll(p1, shift=-1) # end points
dp = p2 - p1 # connection vectors
new_points = np.empty(len(p1) * 4, dtype=np.complex128)
new_points[::4] = p1
new_points[1::4] = p1 + dp / 3
new_points[2::4] = p1 + dp * ZR
new_points[3::4] = p1 + dp / 3 * 2
return new_points
points = _koch_snowflake_complex(order)
x, y = points.real, points.imag
return x, y
colors = ['r', 'g', 'b', 'k', 'y', 'c', 'm']
plt.figure(figsize=(15, 5))
for i in range(3):
index1 = int(np.random.random()*7)
index2 = int(np.random.random()*7)
plt.subplot(1, 3, i+1)
x, y = koch_snowflake(2+i)
plt.fill(x, y, facecolor=colors[index1], edgecolor=colors[index2], linewidth=3)
plt.show()
In [426]:
import matplotlib.pyplot as plt
def handle_close(evt):
print('Closed Figure!')
fig = plt.figure()
fig.canvas.mpl_connect('close_event', handle_close)
plt.text(0.35, 0.5, 'Close Me!', dict(size=30))
plt.show()
In [2]:
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
t = np.linspace(0, 10, 1001)
s = np.sin(t**2) * np.exp(-(t-t[500])**2)
def format_axes(fig):
for i, ax in enumerate(fig.axes):
ax.text(0.5, 0.5, "ax%d" % (i+1), va="center", ha="center")
ax.tick_params(labelbottom=False, labelleft=False)
fig = plt.figure(constrained_layout=True, figsize=(12, 10))
gs = GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :])
ax1.plot(t, s, linewidth=2)
# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))
ax2 = fig.add_subplot(gs[1, :-1])
ax2.plot(t, s, linewidth=2)
ax3 = fig.add_subplot(gs[1:, -1])
ax3.plot(t, s, linewidth=2)
ax4 = fig.add_subplot(gs[-1, 0])
ax4.plot(t, s, linewidth=2)
ax5 = fig.add_subplot(gs[-1, -2])
ax5.plot(t, s, linewidth=2)
fig.suptitle("GridSpec")
format_axes(fig)
plt.show()
In [39]:
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
t = np.linspace(0, 10, 1001)
s = np.sin(t**2) * np.exp(-(t-t[500])**2)
def format_axes(fig):
for i, ax in enumerate(fig.axes):
ax.text(0.5, 0.5, "ax%d" % (i+1), va="center", ha="center")
ax.tick_params(labelbottom=False, labelleft=False)
fig = plt.figure(constrained_layout=True, figsize=(12, 10))
gs = GridSpec(3, 3, figure=fig)
plt.xkcd()
ax1 = fig.add_subplot(gs[0, :])
ax1.plot(t, s, linewidth=2)
# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))
ax2 = fig.add_subplot(gs[1, :-1])
ax2.plot(t, s, linewidth=2)
ax3 = fig.add_subplot(gs[1:, -1])
ax3.plot(t, s, linewidth=2)
ax4 = fig.add_subplot(gs[-1, 0])
ax4.plot(t, s, linewidth=2)
ax5 = fig.add_subplot(gs[-1, -2])
ax5.plot(t, s, linewidth=2)
fig.suptitle("GridSpec")
format_axes(fig)
plt.show()
In [38]:
import numpy as np
import matplotlib.pyplot as plt
mean = [0, 0]
cov = [[1, 1], [1, 2]]
x, y = np.random.multivariate_normal(mean, cov, 3000).T
v = np.sin(x**2+y**2)
#设置坐标轴和网格配置方式
fig = plt.figure(figsize=(12, 8))
grid = plt.GridSpec(4,4, hspace=0.2, wspace=0.2)
main_ax = fig.add_subplot(grid[:-1, 1:])
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey = main_ax)
x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex = main_ax)
#主坐标轴画散点图
main_ax.scatter(x, y, s=200, c=v, marker=r'$\spadesuit$', alpha=0.2, cmap='jet')
#次坐标轴画频次直方图
x_hist.hist(x, 40, histtype='stepfilled', orientation='vertical', facecolor='b', edgecolor='k')
x_hist.invert_yaxis()
y_hist.hist(y, 40, histtype='stepfilled', orientation='horizontal', facecolor='r', edgecolor='k')
y_hist.invert_xaxis()
plt.show()
In [20]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
s = np.sin(t**2) * np.exp(-(t-t[500])**2)
plt.figure(figsize=(12, 8))
ax1 = plt.axes()
plt.plot(t, s, linewidth=2, color='r')
plt.text(5, 0, 'ax1', fontsize=25)
ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])
plt.plot(t, s, linewidth=2, color='r')
plt.text(2, -0.5, 'ax2', fontsize=15)
ax3 = plt.axes([0.2, 0.2, 0.2, 0.2])
plt.plot(t, s, linewidth=2, color='r')
plt.text(2, -0.5, 'ax3', fontsize=15)
ax4 = plt.axes([0.65, 0.2, 0.2, 0.2])
plt.plot(t, s, linewidth=2, color='r')
plt.text(2, -0.5, 'ax4', fontsize=15)
ax5 = plt.axes([0.2, 0.65, 0.2, 0.2])
plt.plot(t, s, linewidth=2, color='r')
plt.text(2, -0.5, 'ax5', fontsize=15)
plt.show()
In [22]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
s = np.sin(t**2) * np.exp(-(t-t[500])**2)
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], xticklabels=[])
ax1.plot(t, s, linewidth=2, color='r')
ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4])
ax2.plot(t, s, linewidth=2, color='b')
plt.show()
In [29]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
s = np.sin(t**2) * np.exp(-(t-t[500])**2)
fig, ax = plt.subplots(2, 3, sharex='col', sharey='row', figsize=(12, 8))
for i in range(2):
for j in range(3):
ax[i, j].plot(t, s, linewidth=2, color='r')
ax[i, j].text(2.5, 0.5, str((i, j)),fontsize=18, ha='center')
plt.show()
In [4]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
s = np.sin(t**2) * np.exp(-(t-t[500])**2)
plt.style.use('ggplot')
fig, ax = plt.subplots(2, 3, sharex='col', sharey='row', figsize=(12, 8))
for i in range(2):
for j in range(3):
ax[i, j].plot(t, s, linewidth=2, color='g')
ax[i, j].text(2.5, 0.5, str((i, j)),fontsize=18, ha='center')
plt.suptitle('ggplot style', fontsize=25)
plt.show()
In [433]:
import matplotlib.pyplot as plt
np.random.seed(1)
x = np.arange(5)
y = np.random.randn(5)
fig, axes = plt.subplots(ncols=2, figsize=(12, 5))
vert_bars = axes[0].bar(x, y, color='b', align='center')
horiz_bars = axes[1].barh(x, y, color='r', align='center')
axes[0].axhline(0, color='gray', linewidth=2)
axes[1].axvline(0, color='gray', linewidth=2)
plt.show()
In [6]:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(19680801)
mu1, sigma1 = 100, 15
mu2, sigma2 = 80, 15
x1 = mu1 + sigma1 * np.random.randn(10000)
x2 = mu2 + sigma2 * np.random.randn(10000)
# the histogram of the data
# 50：将数据分成50组
# facecolor：颜色；alpha：透明度
# density：是密度而不是具体数值
plt.figure(figsize=(10, 8))
n1, bins1, patches1 = plt.hist(x1, 50, density=True, facecolor='g', alpha=1)
n2, bins2, patches2 = plt.hist(x2, 50, density=True, facecolor='r', alpha=0.2)
# n：概率值；bins：具体数值；patches：直方图对象。
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(110, .025, r'$\mu=100,\ \sigma=15$')
plt.text(50, .025, r'$\mu=80,\ \sigma=15$')
# 设置x，y轴的具体范围
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
In [435]:
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
n_bins = 10
x = np.random.randn(1000, 3)
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 8))
ax0, ax1, ax2, ax3 = axes.flatten()
colors = ['red', 'tan', 'lime']
ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)
ax0.legend(prop={'size': 10})
ax0.set_title('bars with legend')
ax1.hist(x, n_bins, density=True, histtype='barstacked')
ax1.set_title('stacked bar')
ax2.hist(x, histtype='barstacked', rwidth=0.9)
ax3.hist(x[:, 0], rwidth=0.9)
ax3.set_title('different sample sizes')
fig.tight_layout()
plt.show()
In [9]:
import numpy as np
import matplotlib.pyplot as plt
size = 5
a = np.random.random(size)
b = np.random.random(size)
c = np.random.random(size)
x = np.arange(size)
# 这里使用的是偏移
plt.figure(figsize=(12, 9))
plt.bar(x, a, width=0.5, label='a',fc='r')
plt.bar(x, b, bottom=a, width=0.5, label='b', fc='g')
plt.bar(x, c, bottom=a+b, width=0.5, label='c', fc='b')
plt.ylim(0, 2.5)
plt.legend()
plt.grid(True)
plt.title('Stacking histograms', fontsize=25)
plt.show()
In [39]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
s1 = np.sin(t**2) * np.exp(-(t-t[500])**2) - 0.5
s2 = np.cos(t**2) * np.exp(-(t-t[500])**2)
plt.figure(figsize=(15, 10))
plt.subplot(211)
plt.plot(t, s1, 'g', linewidth=2)
plt.plot(t, s2, 'b', linewidth=2)
plt.fill_between(t, s1, s2, color='r')
plt.subplot(212)
plt.fill_betweenx(t, t[100], t[200], color='g')
plt.fill_betweenx(t, t[500], t[700], color='r')
plt.fill_between(t, 5, 7, color='b', alpha=0.5)
plt.xlim(t[0], t[-1])
plt.show()
In [90]:
import numpy as np
import matplotlib.pyplot as plt
n = 11
m = 10
t1 = 5
t = np.linspace(0, t1, n)
d = np.random.random((m, n))
plt.figure(figsize=(15, 6))
for i in range(10):
if i < 1:
plt.fill_between(t, np.zeros(n), d[i], label=str(i+1))
else:
plt.fill_between(t, d[i-1], d[i], label=str(i+1))
plt.legend()
plt.xlim(0, 5.5)
plt.ylim(0, 1)
plt.show()
In [38]:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
import scipy.io as sio
t = np.linspace(0,10,2000)
z = np.zeros([5,2000])
for i in range(5):
z[i,:] = np.sin(2*np.pi*t*(i+1)*0.2)
x = [np.linspace(0,9,5)]*2000
y = [np.linspace(0,1999,2000)]*5
x = np.array(x).T
y = np.array(y)
fig = plt.figure(figsize=(6,2.5))
ax = fig.gca(projection='3d')
plt.gca().patch.set_facecolor('white')
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
for i in range(5):
ax.plot(x[i,:],y[i,:],z[i,:])
ax.view_init(20,45)
fig.set_size_inches(12, 7)
plt.show()
In [12]:
import matplotlib.pyplot as plt
import numpy as np
n = 201
t1 = 2
t = np.linspace(0, t1, n)
s = t**3 + np.random.random(n)
n1 = np.random.random(n) + s - 1.5
n2 = np.random.random(n) + s + 0.5
plt.figure(figsize=(15, 6))
plt.fill_between(t, n1, n2, facecolor='r', alpha=0.25)
plt.plot(t, s, 'r', linewidth=2)
plt.xlabel('Time [s]', fontsize=20)
plt.ylabel('Amplitude', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.show()
In [68]:
import numpy as np
import matplotlib.pyplot as plt
nl = 301
t1 = 3
ns = 250
t = np.linspace(0, 3, nl)
s = np.array([np.sin(2*np.pi*(t[i])) if i >= 150 and i < 200 else 0 for i in range(nl)])
#s = np.sin(np.pi*t)
t = t[: ns]
n = 75
plt.figure(figsize=(6, 8))
for i in range(n):
index = int(np.random.random() * 40)
ss = abs(s[index: index+ns]) + i
ss2 = np.ones(ns)*i
#plt.plot(t, ss+i)
#plt.plot(t, ss2, 'k', linewidth=2)
plt.fill_between(t, ss, ss2, facecolor='b', edgecolor='k', linewidth=1)
plt.show()
In [15]:
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 10, 1001)
n1 = np.random.random(1001)
n2 = np.random.random(1001)
e = np.exp(-t)
en1 = e + (n1-n1.mean())/5.
en2 = e + (n2-n2.mean())/5.
e1 = en1 - en1.max()/5
e2 = en1 + en1.max()/5
plt.figure(figsize=(15,10))
plt.fill_between(t, e1, e2, facecolor='green', alpha=0.5)
plt.subplot(111)
plt.plot(t, en1, 'b', linewidth=1, label=r'$Noised1 : e^{-t}$')
plt.subplot(111)
plt.plot(t, en2, 'r', linewidth=1, label=r'$Noised2 : e^{-t} $')
plt.legend(loc='best', fontsize=20)
plt.xlim(0, 10)
plt.grid()
plt.show()
In [16]:
import numpy as np
import matplotlib.pyplot as plt
# 设置每环的宽度
size = 0.3
vals = np.array([[60., 32.], [37., 40.], [29., 10.]])
# 通过get_cmap随机获取颜色
cmap = plt.get_cmap("tab20c")
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))
print(vals.sum(axis=1))
# [92. 77. 39.]
plt.figure(figsize=(12, 9))
plt.pie(vals.sum(axis=1), radius=1, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w'))
print(vals.flatten())
# [60. 32. 37. 40. 29. 10.]
plt.pie(vals.flatten(), radius=1-size, colors=inner_colors,
wedgeprops=dict(width=size, edgecolor='w'))
# equal 使得为正圆
plt.axis('equal')
plt.show()
In [16]:
import numpy as np
import matplotlib.pyplot as plt
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 8))
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True)
ax1.axis('equal')
ax2.pie(sizes, autopct='%1.2f%%', shadow=True, startangle=90, explode=explode,
pctdistance=1.12)
ax2.axis('equal')
ax2.legend(labels=labels, loc='upper right')
plt.show()
In [19]:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(19680801)
N = 10
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
plt.figure(figsize=(12, 9))
ax = plt.subplot(111, projection='polar')
bars = ax.bar(theta, radii, width=width, bottom=0.0)
# left表示从哪开始，
# radii表示从中心点向边缘绘制的长度（半径）
# width表示末端的弧长
# 自定义颜色和不透明度
for r, bar in zip(radii, bars):
bar.set_facecolor(plt.cm.jet(r / 10.))
bar.set_alpha(0.9)
plt.show()
In [7]:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.rand(10)
data2 = np.random.rand(10, 10)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 7))
ax1.boxplot(data)
ax2.boxplot(data2, vert=False)
plt.show()
In [28]:
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(vmin=5, vmax=10)
cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
orientation='horizontal')
cb1.set_label('Some Units')
fig.show()
In [37]:
# import matplotlib.pyplot as plt
# import numpy as np
# from matplotlib import animation
# fig, ax = plt.subplots()
# x = np.arange(0, 4 * np.pi, 0.01)
# line, = ax.plot(x, np.sin(x))
# def animate(i):
# line.set_ydata(np.sin(x + i / 200))
# return line,
# def init():
# line.set_ydata(np.sin(x))
# return line,
# ani = animation.FuncAnimation(fig=fig, func=animate, frames=100,
# init_func=init, interval=20, blit=False)
# plt.show()
In [8]:
import matplotlib.pyplot as plt
import matplotlib as mpl
cmap = mpl.colors.Colormap('jet')
print(cmap.N)
In [15]:
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
client = Client('IRIS')
lt = 2500
starttime = UTCDateTime('2017-12-15T16:47:56.000')
st = client.get_waveforms('IC', 'XAN', '*', 'BH?', starttime, starttime+lt)
st.filter('bandpass', freqmin=0.005, freqmax=0.05, zerophase=True)
st.plot(color='r', linewidth=0.75, alpha=0.2)
Out[15]:
In [20]:
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
client = Client('IRIS')
lt = 1500
t = UTCDateTime("2017-11-12T18:18:19.000")
st = client.get_waveforms('IC', 'XAN', '*', 'BH?', t+300, t+lt)
st.filter('bandpass', freqmin=0.005, freqmax=0.1, zerophase=True)
st.plot(color='r', linewidth=0.75, alpha=0.2)
Out[20]:
In [2]:
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
import numpy as np
import matplotlib.pyplot as plt
starttime = UTCDateTime('2019-08-01T00:00:00.000')
endtime = UTCDateTime('2019-08-20T23:59:59.999')
minlongitude=100
maxlongitude=140
minlatitude=10
maxlatitude=45
client = Client('IRIS')
inventory = client.get_stations(minlongitude=minlongitude,
maxlongitude=maxlongitude,
minlatitude=minlatitude,
maxlatitude=maxlatitude,
starttime=starttime,
endtime=endtime)
#print(inventory)
coord = list()
with open('sta.txt', 'w') as fin:
for net in inventory:
for sta in net:
coord.append([sta.longitude, sta.latitude, sta.elevation])
out = ' '.join([net.code, sta.code, str(sta.longitude), str(sta.latitude), str(sta.elevation), '\n'])
fin.write(out)
# print(net.code)
# print(sta.code)
# print(sta.latitude)
# print(sta.longitude)
# print(sta.elevation)
# print('-----------------------')
coord = np.array(coord)
center = np.mean(coord, axis=0)
print(center)
dist = ((coord[:, 0]-center[0])**2+(coord[:, 1]-center[1])**2)**0.5
plt.figure(figsize=(9, 8.5))
plt.scatter(coord[:, 0], coord[:, 1], c=dist, s=200, marker='v', cmap='jet')
plt.xlim(minlongitude-maxlongitude*0.1, maxlongitude+maxlongitude*0.1)
plt.ylim(minlatitude-maxlatitude*0.1, maxlatitude+maxlatitude*0.1)
plt.xlabel('Longitude [$^o$]', fontsize=20)
plt.ylabel('Latitude [$^o$]', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.title('Get station info. from IRIS', fontsize=25)
plt.grid(True)
cbar=plt.colorbar(extend='both')
cbar.set_label('Distance [$^o$]')
plt.show()
print(len(coord))
In [81]:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
fig = plt.figure(figsize=(8, 7))
ax = fig.add_subplot(111, projection='3d')
X = np.random.random(4)
Y = np.random.random(4)
Z = np.random.random(4)
ax.plot_trisurf(X, Y, Z)
plt.show()
In [80]:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
data = np.random.randint(0, 255, size=[40, 40, 40])
x, y, z = data[0], data[1], data[2]
plt.figure(figsize=(7, 7))
ax = plt.subplot(projection='3d') # 创建一个三维的绘图工程
# 将数据点分成三部分画，在颜色上有区分度
ax.scatter(x[:10], y[:10], z[:10], c='y', s=100) # 绘制数据点
ax.scatter(x[10:20], y[10:20], z[10:20], c='r', s=100)
ax.scatter(x[30:40], y[30:40], z[30:40], c='g', s=100)
ax.set_zlabel('Z') # 坐标轴
ax.set_ylabel('Y')
ax.set_xlabel('X')
fig=plt.figure(figsize=(7, 7))
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
# 具体函数方法可用 help(function) 查看，如：help(ax.plot_surface)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='rainbow')
plt.show()
In [75]:
import numpy as np
with open('sta.txt') as fin:
print(fin.readlines()[0])
In [50]:
from pylab import *
eqs = []
eqs.append((r'$\int_{-\infty}^{\infty}f(x)e^{-i\omega x}dx $'))
axes([0.025, 0.025, 0.95, 0.95])
text(0.5, 0.85, eqs[0], ha='center', va='center', color='r', alpha=1, transform=gca().transAxes,
fontsize=20, clip_on=True)
text(0.5, 0.6, eqs[0], ha='center', va='center', color='b', alpha=1, transform=gca().transAxes,
fontsize=20, clip_on=True)
text(0.5, 0.4, eqs[0], ha='center', va='center', color='k', alpha=1, transform=gca().transAxes,
fontsize=20, clip_on=True)
text(0.5, 0.15, r'$F(k) = \sum_{n=0}^{N-1}x(n)e^{\frac{-i \pi 2nk}{N}}$', ha='center', va='center',\
color='k', alpha=1, transform=gca().transAxes, fontsize=25, clip_on=True)
xticks([])
yticks([])
title('Mathematical equations', fontsize=25)
show()
#savefig('eqs.pdf
In [4]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 5.5))
plt.subplot(131)
map = Basemap(projection='cyl', lat_0=0, lon_0=0)
map.drawcoastlines(color='blue')
plt.title('coastlines', fontsize=20)
plt.subplot(132)
map = Basemap()
map.drawcoastlines(color='black')
map.drawcountries(color='red')
plt.title('coastlines and countries\' lines', fontsize=20)
plt.subplot(133)
map=Basemap()
map.drawcoastlines(color='black')
map.drawstates(color='green')
map.drawrivers(color='blue')
plt.title('coastlines, states\' lines and rivers', fontsize=20)
plt.suptitle('Simple geoMap', fontsize=35)
plt.show()
In [6]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
map = Basemap(projection = 'cyl')
map.drawmapboundary(fill_color = 'aqua')
map.fillcontinents(color = 'coral', lake_color = 'aqua')
map.drawcoastlines()
plt.title('fill continents and oceans', fontsize=30)
plt.show()
In [8]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
# set resolution=None to skip processing of boundary datasets.
plt.figure(figsize=(20, 6))
plt.subplot(131)
m = Basemap(width=12000000,height=9000000,projection='lcc',
resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.bluemarble(scale=0.5)
plt.title('m.bluemarble()', fontsize=20)
plt.subplot(132)
m = Basemap(width=12000000,height=9000000,projection='lcc',
resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.shadedrelief(scale=0.5)
plt.title('m.shadedrelief()', fontsize=20)
plt.subplot(133)
m = Basemap(width=12000000,height=9000000,projection='lcc',
resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.etopo(scale=0.5)
plt.title('m.etopo()', fontsize=20)
plt.suptitle('Basemap', fontsize=30)
plt.xticks(fontsize=20)
plt.show()
In [10]:
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 8))
m = Basemap(width=12000000,height=9000000,projection='lcc',
resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.bluemarble()
parallels = np.arange(0.,90,10.)
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=20)
meridians = np.arange(180.,360.,10.)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=20)
plt.title('setting ticks of geoMap', fontsize=30)
plt.show()
In [12]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
map = Basemap(llcrnrlon=3.75,llcrnrlat=39.75,urcrnrlon=4.35,urcrnrlat=40.15, epsg=5520)
map.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500, verbose= True)
plt.title('Get image from ArcGIS', fontsize=30)
plt.show()
In [38]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 13))
plt.subplot(341)
map = Basemap(projection='ortho', lat_0=0, lon_0=0)
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='coral', lake_color='aqua')
map.drawcoastlines(color='black')
plt.title('ortho projection', fontsize=20)
plt.subplot(342)
map = Basemap(projection='hammer', lat_0=0, lon_0=0)
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='coral', lake_color='aqua')
map.drawcoastlines(color='black')
plt.title('hammer projection', fontsize=20)
plt.subplot(343)
map = Basemap(projection = 'aeqd', lon_0 = 10, lat_0 = 50)
map.drawmapboundary(fill_color = 'aqua')
map.fillcontinents(color = 'coral', lake_color = 'aqua')
map.drawcoastlines()
plt.title('aeqd projection', fontsize=20)
plt.subplot(344)
m = Basemap(width=28000000, height=28000000, projection='gnom', lat_0=0, lon_0=0)
m.drawmapboundary(fill_color='white')
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color='gray',lake_color='white')
m.drawparallels(np.arange(-80,81,20))
m.drawmeridians(np.arange(-180,180,20))
plt.title('gnom Projection', fontsize=20)
plt.subplot(345)
m = Basemap(projection='moll',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,420.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title('moll Projection', fontsize=20)
plt.subplot(346)
m = Basemap(projection='robin',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,360.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("robin Projection", fontsize=20)
plt.subplot(347)
m = Basemap(projection='eck4',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,360.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("eck4 Projection", fontsize=20)
plt.subplot(348)
m = Basemap(projection='kav7',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,360.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("kav7 Projection", fontsize=20)
plt.subplot(349)
m = Basemap(projection='mbtfpq',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,360.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("mbtfpq Projection", fontsize=20)
plt.subplot(3, 4, 10)
m = Basemap(projection='sinu',lon_0=0,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,120.,30.))
m.drawmeridians(np.arange(0.,360.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("sinu projection", fontsize=20)
plt.subplot(3, 4, 11)
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\
llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90.,91.,30.))
m.drawmeridians(np.arange(-180.,181.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("merc Projection", fontsize=20)
plt.show()
In [16]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(9, 6))
map = Basemap(projection='ortho', lat_0=32, lon_0=120)
map.bluemarble()
plt.title('ortho projection and \'bluemarble\' cpt', fontsize=30)
plt.show()
In [18]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
plt.figure(figsize=(5, 6))
map = Basemap(llcrnrlon = 119.3, llcrnrlat = 20.7, urcrnrlon = 124.6, urcrnrlat = 26,
resolution = 'h', epsg = 3415)
map.drawmapboundary(fill_color = 'aqua')
map.fillcontinents(color = '#003300', lake_color = '#0000FF')
map.drawcoastlines()
plt.show()
In [20]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
sta_lat = np.linspace(30,50, 5)
sta_lon = np.linspace(-120,-80,5)
plt.figure(figsize=(12, 8))
m = Basemap(width=12000000,height=9000000,projection='lcc',
resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.shadedrelief()
for i, j in zip(sta_lon, sta_lat):
x, y = m(i, j)
m.scatter(x, y, marker='v', s=400, color='k')
x, y = m(-70, 10)
m.scatter(x, y, marker='*', s=400, color='r')
parallels = np.arange(0.,90,10.)
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=20)
meridians = np.arange(180.,360.,10.)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=20)
plt.title('epicenter and stations', fontsize=30)
plt.show()
In [21]:
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from datetime import datetime
plt.figure(figsize=(10, 6))
# miller projection
map = Basemap(projection='mill',lon_0=180)
# plot coastlines, draw label meridians and parallels.
map.drawcoastlines()
map.drawparallels(np.arange(-90,90,30),labels=[1,0,0,0], fontsize=20)
map.drawmeridians(np.arange(map.lonmin,map.lonmax+30,60),labels=[0,0,0,1], fontsize=20)
# fill continents 'coral' (with zorder=0), color wet areas 'aqua'
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='coral',lake_color='aqua')
# shade the night areas, with alpha transparency so the
# map shows through. Use current time in UTC.
date = datetime.utcnow()
CS=map.nightshade(date)
plt.title('Day/Night Map for %s (UTC)' % date.strftime("%d %b %Y %H:%M:%S"), fontsize=30)
plt.show()
In [43]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
lon = np.linspace(-100, 100, 21)
lat = np.zeros(21)
plt.figure(figsize=(9, 6))
map = Basemap(projection='hammer', lat_0=0, lon_0=0)
map.drawmapboundary(fill_color='aqua')
#map.fillcontinents(color='gray', lake_color='aqua')
map.drawcoastlines(color='black')
x, y = map(lon, lat)
map.scatter(x, y, marker='o', s=200, color='r')
x, y = map(-100, 0)
plt.text(x-5000000, y+500000, 'station', fontsize=25, color='blue')
plt.title('plt.text()', fontsize=25)
plt.show()
In [48]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
lon = np.random.random(21)*100-50
lat = np.random.random(21)*80-40
x = -100
y = 30
plt.figure(figsize=(12, 9))
map = Basemap(projection='kav7', lon_0=0, resolution='c')
map.drawcoastlines(color='k', linewidth=1)
#map.fillcontinents(color='#EEEEEE', lake_color='aqua')
for i, j in zip(lon, lat):
map.drawgreatcircle(i, j, x, y, del_s=111.195, linewidth=2)
i, j = map(lon, lat)
m, n = map(x, y)
map.scatter(i, j, marker='v', s=300, color='k')
map.scatter(m, n, marker='*', s=500, color='red')
'''
x = np.linspace(-50, 50, 11)
y = np.linspace(-30, 30, 21)
X, Y = np.meshgrid(x, y)
v = np.random.random((21,11))
plt.figure(figsize=(8,6))
map.pcolormesh(X, Y, v, cmap='hot')
map.colorbar(extend='both')
'''
parallels = np.linspace(-90, 90, 7)
meridians = np.linspace(0, 360, 7)
map.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=16)
map.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=16)
plt.title('plot great circle arc', fontsize=25)
plt.show()
In [52]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
plt.figure(figsize=(20, 7.5))
plt.subplot(121)
map = Basemap(projection='ortho',lat_0=45,lon_0=-100,resolution='l')
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
map.fillcontinents(color='#EEEEEE',lake_color='aqua')
# draw the edge of the map projection region (the projection limb)
map.drawmapboundary(fill_color='aqua')
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0,360,30))
map.drawparallels(np.arange(-90,90,30))
# make up some data on a regular lat/lon grid.
nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
lons = (delta*np.indices((nlats,nlons))[1,:,:])
wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
# compute native map projection coordinates of lat/lon grid.
x, y = map(lons*180./np.pi, lats*180./np.pi)
# contour data over the map.
map.contourf(x,y,wave+mean, cmap='viridis')
plt.colorbar(extend='both', shrink=0.75)
plt.subplot(122)
map = Basemap(projection='ortho',lat_0=45,lon_0=-100,resolution='l')
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
map.fillcontinents(color='#EEEEEE',lake_color='aqua')
# draw the edge of the map projection region (the projection limb)
map.drawmapboundary(fill_color='aqua')
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0,360,30))
map.drawparallels(np.arange(-90,90,30))
# make up some data on a regular lat/lon grid.
nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
lons = (delta*np.indices((nlats,nlons))[1,:,:])
wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
# compute native map projection coordinates of lat/lon grid.
x, y = map(lons*180./np.pi, lats*180./np.pi)
# contour data over the map.
map.contour(x,y,wave+mean, cmap='jet')
plt.suptitle('contour lines over filled continent background', fontsize=20)
plt.show()
In [55]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
fig=plt.figure(figsize=(13, 6))
map = Basemap(projection='robin',
lat_0=0, lon_0=0)
lons = np.arange(-180, 190, 30)
lats = np.arange(-90, 100, 15)
data = np.indices((lats.shape[0], lons.shape[0]))
data = data[0] + data[1]
llons, llats = np.meshgrid(lons, lats)
ax = fig.add_subplot(121)
ax.set_title('Without transform_scalar', fontsize=20)
x, y = map(llons, llats)
map.pcolormesh(x, y, data, cmap='jet')
map.drawcoastlines()
ax = fig.add_subplot(122)
ax.set_title('Applying transform_scalar', fontsize=20)
data_interp, x, y = map.transform_scalar(data, lons, lats, 50, 30, returnxy=True, masked=True)
map.pcolormesh(x, y, data_interp, cmap='jet')
map.drawcoastlines()
plt.show()
In [57]:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
x = np.linspace(-180, 180, 361)
y = np.linspace(-90, 90, 181)
X, Y = np.meshgrid(x, y)
v = np.sqrt(X**2+Y**2)
plt.figure(figsize=(10, 7))
map = Basemap(projection='hammer', lat_0=0, lon_0=0)
x, y = map(X, Y)
map.pcolormesh(x, y, v, cmap='RdBu_r')
map.drawcoastlines(color='k', linewidth=1)
map.drawmapboundary(color='k')
plt.colorbar(extend='both', shrink=0.45)
plt.title('Extra data plotted on basic map', fontsize=25)
plt.show()
In [59]:
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
x = np.linspace(-90, 90, 91)
y = np.linspace(-90, 90, 91)
X, Y = np.meshgrid(x,y)
v = np.exp(-X**2/10-Y**2/2) + np.sqrt(X**2+Y**2) #+ np.random.random((101, 121))*10
plt.figure(figsize=(10, 7))
map = Basemap(projection='ortho', lat_0=0, lon_0=0)
x, y = map(X, Y)
map.pcolormesh(x, y, v, cmap='nipy_spectral')
map.drawcoastlines(linewidth=1, color='k')
map.fillcontinents(color='#CCCCCC')
plt.colorbar(extend='both', shrink=0.5)
plt.title('Plot extra data on basemap', fontsize=20)
plt.show()
